The relational database has the limitation of expressing human knowledge because it cannot incorporate semantics into the data model. Semantic technology is a new way of storing information, which combines data representation and human knowledge into a machine-readable format. This can be achieved by RDF/OWL and triple store technologies. From our previous Proof of Concept presentations in this forum, we showed exploratory Enterprise Knowledge Graph (EKG) transforming conventional Relational Datastore into triples using FIBO, an industry standard ontology expressed in RDF/OWL. EKG is built on top of two pillars of knowledge base: the data from legacy systems and human knowledge embedded in (extended) FIBO. It enables "Knowledge Analytics" utilizing the power of “Logical Inference.”

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The rise of “Machine Intelligence” is adding even more opportunities to Knowledge Analytics through probabilistic inferences. In this presentation, we will introduce machine learning to 'augment’ the knowledge base. New insights discovered by machine learning models can be added back to EKG as new knowledge. Logical Inference through RDF/OWL technologies, together with Probabilistic Inference through machine learning, create a powerful combination to extend the knowledge analytics to much higher potential to solve real-world financial industry problems.

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The presentation includes selected use cases for common data issues in financial services firms such as matching master data from different sources, identifying unknown entities and classification of entities, and demonstration of the solution using graph DB technologies and R.